Scene filtering using motion estimation

ABSTRACT

Scene filtering using motion estimation, including identifying, in camera data from an autonomous vehicle, based on motion relative to the autonomous vehicle, one or more pixels; filtering, from the camera data, the one or more pixels; and training, based on the filtered camera data, a neural network.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Application No. 62/908,422, filed Sep. 30, 2019, which is hereby incorporated by reference in its entirety.

BACKGROUND Field of the Invention

The field of the invention is machine learning, or, more specifically, methods, apparatus, and products for scene filtering using motion estimation.

Description Of Related Art

To train a neural network for a driving scenario, the network will learn substantially faster and better if it is possible to label important vs. unimportant data. Typically this is achieved by humans labeling relevant objects manually, a time consuming, expensive, and error-prone process.

SUMMARY

Scene filtering using motion estimation may include identifying, in camera data from an autonomous vehicle, based on motion relative to the autonomous vehicle, one or more pixels; filtering, from the camera data, the one or more pixels; and training, based on the filtered camera data, a neural network.

The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows example views of an autonomous vehicle for scene filtering using motion estimation.

FIG. 2 is block diagram of an autonomous computing system for scene filtering using motion estimation.

FIG. 3 is a block diagram of a redundant power fabric for scene filtering using motion estimation.

FIG. 4 is a block diagram of a redundant data fabric for scene filtering using motion estimation.

FIG. 5 is an example view of process allocation across CPU packages for scene filtering using motion estimation.

FIG. 6 is a block diagram of an example execution environment for scene filtering using motion estimation.

FIG. 7 is a flowchart of an example method for scene filtering using motion estimation.

FIG. 8 is a flowchart of an example method for scene filtering using motion estimation.

FIG. 9 is a flowchart of an example method for scene filtering using motion estimation.

DETAILED DESCRIPTION

Scene filtering using motion estimation may be implemented in an autonomous vehicle. Accordingly, FIG. 1 shows multiple views of an autonomous vehicle 100 configured for scene filtering using motion estimation according to embodiments of the present invention. Right side view 101 a shows a right side of the autonomous vehicle 100. Shown in the right side view 101 a are cameras 102 and 103, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the right side of the car. Front view 101 b shows a front side of the autonomous vehicle 100. Shown in the front view 101 b are cameras 104 and 106, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the front of the car. Rear view 101 c shows a rear side of the autonomous vehicle 100. Shown in the rear view 101 c are cameras 108 and 110, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the rear of the car. Top view 101 d shows a rear side of the autonomous vehicle 100. Shown in the top view 101 d are cameras 102-110. Also shown are cameras 112 and 114, configured to capture image data, video data, and/or audio data of the environmental state of the autonomous vehicle 100 from the perspective of the left side of the car.

Further shown in the top view 101 d is an automation computing system 116. The automation computing system 116 comprises one or more computing devices configured to control one or more autonomous operations (e.g., autonomous driving operations) of the autonomous vehicle 100. For example, the automation computing system 116 may be configured to process sensor data (e.g., data from the cameras 102-114 and potentially other sensors), operational data (e.g., a speed, acceleration, gear, orientation, turning direction), and other data to determine a operational state and/or operational history of the autonomous vehicle. The automation computing system 116 may then determine one or more operational commands for the autonomous vehicle (e.g., a change in speed or acceleration, a change in brake application, a change in gear, a change in turning or orientation, etc.). The automation computing system 116 may also capture and store sensor data. Operational data of the autonomous vehicle may also be stored in association with corresponding sensor data, thereby indicating the operational data of the autonomous vehicle 100 at the time the sensor data was captured.

Although the autonomous vehicle 100 if FIG. 1 is shown as car, it is understood that autonomous vehicles 100 configured for scene filtering using motion estimation may also include other vehicles, including motorcycles, planes, helicopters, unmanned aerial vehicles (UAVs, e.g., drones), or other vehicles as can be appreciated. Moreover, it is understood that additional cameras or other external sensors may also be included in the autonomous vehicle 100.

Scene filtering using motion estimation in accordance with the present invention is generally implemented with computers, that is, with automated computing machinery. For further explanation, therefore, FIG. 2 sets forth a block diagram of automated computing machinery comprising an exemplary automation computing system 116 configured for scene filtering using motion estimation according to embodiments of the present invention. The automation computing system 116 of FIG. 2 includes at least one computer Central Processing Unit (CPU) package 204 as well as random access memory 206 (‘RAM’) which is connected through a high speed memory bus 208 and bus adapter 210 to CPU packages 204 via a front side bus 211 and to other components of the automation computing system 116.

A CPU package 204 may comprise a plurality of processing units. For example, each CPU package 204 may comprise a logical or physical grouping of a plurality of processing units. Each processing unit may be allocated a particular process for execution. Moreover, each CPU package 204 may comprise one or more redundant processing units. A redundant processing unit is a processing unit not allocated a particular process for execution unless a failure occurs in another processing unit. For example, when a given processing unit allocated a particular process fails, a redundant processing unit may be selected and allocated the given process. A process may be allocated to a plurality of processing units within the same CPU package 204 or different CPU packages 204. For example, a given process may be allocated to a primary processing unit in a CPU package 204. The results or output of the given process may be output from the primary processing unit to a receiving process or service. The given process may also be executed in parallel on a secondary processing unit. The secondary processing unit may be included within the same CPU package 204 or a different CPU package 204. The secondary processing unit may not provide its output or results of the process until the primary processing unit fails. The receiving process or service will then receive data from the secondary processing unit. A redundant processing unit may then be selected and have allocated the given process to ensure that two or more processing units are allocated the given process for redundancy and increased reliability.

The CPU packages 204 are communicatively coupled to one or more sensors 212. The sensors 212 are configured to capture sensor data describing the operational and environmental conditions of an autonomous vehicle. For example, the sensors 212 may include cameras (e.g., the cameras 102-114 of FIG. 1 ), accelerometers, Global Positioning System (GPS) radios, Lidar sensors, or other sensors as can be appreciated. As described herein, cameras may include a stolid state sensor 212 with a solid state shutter capable of measuring photons or a time of flight of photons. For example, a camera may be configured to capture or measure photons captured via the shutter for encoding as images and/or video data. As another example, a camera may emit photons and measure the time of flight of the emitted photons. Cameras may also include event cameras configured to measure changes in light and/or motion of light.

Although the sensors 212 are shown as being external to the automation computing system 116, it is understood that one or more of the sensors 212 may reside as a component of the automation computing system 212 (e.g., on the same board, within the same housing or chassis). The sensors 212 may be communicatively coupled with the CPU packages 204 via a switched fabric 213. The switched fabric 213 comprises a communications topology through which the CPU packages 204 and sensors 212 are coupled via a plurality of switching mechanisms (e.g., latches, switches, crossbar switches, field programmable gate arrays (FPGAs), etc.). For example, the switched fabric 213 may implement a mesh connection connecting the CPU packages 204 and sensors 212 as endpoints, with the switching mechanisms serving as intermediary nodes of the mesh connection. The CPU packages 204 and sensors 212 may be in communication via a plurality of switched fabrics 213. For example, each of the switched fabrics 213 may include the CPU packages 204 and sensors 212, or a subset of the CPU packages 204 and sensors 212, as endpoints. Each switched fabric 213 may also comprise a respective plurality of switching components. The switching components of a given switched fabric 213 may be independent (e.g., not connected) of the switching components of other switched fabrics 213 such that only switched fabric 213 endpoints (e.g., the CPU packages 204 and sensors 212) are overlapping across the switched fabrics 213. This provides redundancy such that, should a connection between a CPU package 204 and sensor 212 fail in one switched fabric 213, the CPU package 204 and sensor 212 may remain connected via another switched fabric 213. Moreover, in the event of a failure in a CPU package 204, a processor of a CPU package 204, or a sensor, a communications path excluding the failed component and including a functional redundant component may be established.

The CPU packages 204 and sensors 212 are configured to receive power from one or more power supplies 215. The power supplies 215 may comprise an extension of a power system of the autonomous vehicle 100 or an independent power source (e.g., a battery). The power supplies 215 may supply power to the CPU packages 204 and sensors 212 by another switched fabric 214. The switched fabric 214 provides redundant power pathways such that, in the event of a failure in a power connection, a new power connection pathway may be established to the CPU packages 204 and sensors 214.

Stored in RAM 206 is an automation module 220. The automation module 220 may be configured to process sensor data from the sensors 212 to determine one or more operational commands for an autonomous vehicle 100 to affect the movement, direction, or other function of the autonomous vehicle 100, thereby facilitating autonomous driving or operation of the vehicle. Such operational commands may include a change in the speed of the autonomous vehicle 100, a change in steering direction, a change in gear, or other command as can be appreciated. For example, the automation module 220 may provide sensor data and/or processed sensor data as one or more inputs to a trained machine learning model (e.g., a trained neural network) to determine the one or more operational commands. The operational commands may then be communicated to autonomous vehicle control systems 223 via a vehicle interface 222. The autonomous vehicle control systems 223 are configured to affect the movement and operation of the autonomous vehicle 100. For example, the autonomous vehicle control systems 223 may turn or otherwise change the direction of the autonomous vehicle 100, accelerate or decelerate the autonomous vehicle 100, change a gear of the autonomous vehicle 100, or otherwise affect the movement and operation of the autonomous vehicle 100.

Further stored in RAM 206 is a data collection module 224 configured to process and/or store sensor data received from the one or more sensors 212. For example, the data collection module 224 may store the sensor data as captured by the one or more sensors 212, or processed sensor data 212 (e.g., sensor data 212 having object recognition, compression, depth filtering, or other processes applied). Such processing may be performed by the data collection module 224 in real-time or in substantially real-time as the sensor data is captured by the one or more sensors 212. The processed sensor data may then be used by other functions or modules. For example, the automation module 220 may use processed sensor data as input to determine one or more operational commands. The data collection module 224 may store the sensor data in data storage 218.

Also stored in RAM 206 is a data processing module 226. The data processing module 226 is configured to perform one or more processes on stored sensor data (e.g., stored in data storage 218 by the data collection module 224) prior to upload to an execution environment 227. Such operations can include filtering, compression, encoding, decoding, or other operations as can be appreciated. The data processing module 226 may then communicate the processed and stored sensor data to the execution environment 227.

Further stored in RAM 206 is a hypervisor 228. The hypervisor 228 is configured to manage the configuration and execution of one or more virtual machines 229. For example, each virtual machine 229 may emulate and/or simulate the operation of a computer. Accordingly, each virtual machine 229 may comprise a guest operating system 216 for the simulated computer. The hypervisor 228 may manage the creation of a virtual machine 229 including installation of the guest operating system 216. The hypervisor 228 may also manage when execution of a virtual machine 229 begins, is suspended, is resumed, or is terminated. The hypervisor 228 may also control access to computational resources (e.g., processing resources, memory resources, device resources) by each of the virtual machines.

Each of the virtual machines 229 may be configured to execute one or more of the automation module 220, the data collection module 224, the data processing module 226, or combinations thereof. Moreover, as is set forth above, each of the virtual machines 229 may comprise its own guest operating system 216. Guest operating systems 216 useful in autonomous vehicles in accordance with some embodiments of the present disclosure include UNIX™, Linux™, Microsoft Windows™, AIX™, IBM's i OS™, and others as will occur to those of skill in the art. For example, the autonomous vehicle 100 may be configured to execute a first operating system when the autonomous vehicle is in an autonomous (or even partially autonomous) driving mode and the autonomous vehicle 100 may be configured to execute a second operating system when the autonomous vehicle is not in an autonomous (or even partially autonomous) driving mode. In such an example, the first operating system may be formally verified, secure, and operate in real-time such that data collected from the sensors 212 are processed within a predetermined period of time, and autonomous driving operations are performed within a predetermined period of time, such that data is processed and acted upon essentially in real-time. Continuing with this example, the second operating system may not be formally verified, may be less secure, and may not operate in real-time as the tasks that are carried out (which are described in greater detail below) by the second operating system are not as time-sensitive the tasks (e.g., carrying out self-driving operations) performed by the first operating system.

Readers will appreciate that although the example included in the preceding paragraph relates to an embodiment where the autonomous vehicle 100 may be configured to execute a first operating system when the autonomous vehicle is in an autonomous (or even partially autonomous) driving mode and the autonomous vehicle 100 may be configured to execute a second operating system when the autonomous vehicle is not in an autonomous (or even partially autonomous) driving mode, other embodiments are within the scope of the present disclosure. For example, in another embodiment one CPU (or other appropriate entity such as a chip, CPU core, and so on) may be executing the first operating system and a second CPU (or other appropriate entity) may be executing the second operating system, where switching between these two modalities is accomplished through fabric switching, as described in greater detail below. Likewise, in some embodiments, processing resources such as a CPU may be partitioned where a first partition supports the execution of the first operating system and a second partition supports the execution of the second operating system.

The guest operating systems 216 may correspond to a particular operating system modality. An operating system modality is a set of parameters or constraints which a given operating system satisfies, and are not satisfied by operating systems of another modality. For example, a given operating system may be considered a “real-time operating system” in that one or more processes executed by the operating system must be performed according to one or more time constraints. For example, as the automation module 220 must make determinations as to operational commands to facilitate autonomous operation of a vehicle. Accordingly, the automation module 220 must make such determinations within one or more time constraints in order for autonomous operation to be performed in real time. The automation module 220 may then be executed in an operating system (e.g., a guest operating system 216 of a virtual machine 229) corresponding to a “real-time operating system” modality. Conversely, the data processing module 226 may be able to perform its processing of sensor data independent of any time constrains, and may then be executed in an operating system (e.g., a guest operating system 216 of a virtual machine 229) corresponding to a “non-real-time operating system” modality.

As another example, an operating system (e.g., a guest operating system 216 of a virtual machine 229) may comprise a formally verified operating system. A formally verified operating system is an operating system for which the correctness of each function and operation has been verified with respect to a formal specification according to formal proofs. A formally verified operating system and an unverified operating system (e.g., one that has not been formally verified according to these proofs) can be said to operate in different modalities.

The automation module 220, data collection module 224, data collection module 224, data processing module 226, hypervisor 228, and virtual machine 229 in the example of FIG. 2 are shown in RAM 206, but many components of such software typically are stored in non-volatile memory also, such as, for example, on data storage 218, such as a disk drive. Moreover, any of the automation module 220, data collection module 224, and data processing module 226 may be executed in a virtual machine 229 and facilitated by a guest operating system 216 of that virtual machine 229.

The automation computing system 116 of FIG. 2 includes disk drive adapter 230 coupled through expansion bus 232 and bus adapter 210 to processor(s) 204 and other components of the automation computing system 116. Disk drive adapter 230 connects non-volatile data storage to the automation computing system 116 in the form of data storage 213. Disk drive adapters 230 useful in computers configured for scene filtering using motion estimation according to embodiments of the present invention include Integrated Drive Electronics (‘IDE’) adapters, Small Computer System Interface (‘SCSI’) adapters, and others as will occur to those of skill in the art. Non-volatile computer memory also may be implemented for as an optical disk drive, electrically erasable programmable read-only memory (so-called ‘EEPROM’ or ‘Flash’ memory), RAM drives, and so on, as will occur to those of skill in the art.

The exemplary automation computing system 116 of FIG. 2 includes a communications adapter 238 for data communications with other computers and for data communications with a data communications network. Such data communications may be carried out serially through RS-238 connections, through external buses such as a Universal Serial Bus (‘USB’), through data communications networks such as IP data communications networks, and in other ways as will occur to those of skill in the art. Communications adapters implement the hardware level of data communications through which one computer sends data communications to another computer, directly or through a data communications network. Examples of communications adapters useful in computers configured for scene filtering using motion estimation according to embodiments of the present invention include modems for wired dial-up communications, Ethernet (IEEE 802.3) adapters for wired data communications, 802.11 adapters for wireless data communications, as well as mobile adapters (e.g., cellular communications adapters) for mobile data communications. For example, the automation computing system 116 may communicate with one or more remotely disposed execution environments 227 via the communications adapter 238.

The exemplary automation computing system of FIG. 2 also includes one or more Artificial Intelligence (AI) accelerators 240. The AI accelerator 240 provides hardware-based assistance and acceleration of AI-related functions, including machine learning, computer vision, etc. Accordingly, performance of any of the automation module 220, data collection module 224, data processing module 226, or other operations of the automation computing system 116 may be performed at least in part by the AI accelerators 240.

The exemplary automation computing system of FIG. 2 also includes one or more graphics processing units (GPUs) 242. The GPUs 242 are configured to provide additional processing and memory resources for processing image and/or video data, including encoding, decoding, etc. Accordingly, performance of any of the automation module 220, data collection module 224, data processing module 226, or other operations of the automation computing system 116 may be performed at least in part by the GPUs 242.

FIG. 3 shows an example redundant power fabric for scene filtering using motion estimation. The redundant power fabric provides redundant pathways for power transfer between the power supplies 215, the sensors 212, and the CPU packages 204. In this example, the power supplies 215 are coupled to the sensors 212 and CPU packages via two switched fabrics 214 a and 214 b. The topology shown in FIG. 3 provides redundant pathways between the power supplies 215, the sensors 212, and the CPU packages 204 such that power can be rerouted through any of multiple pathways in the event of a failure in an active connection pathway. The switched fabrics 214 a and 214 b may provide power to the sensors 212 using various connections, including Mobile Industry Processor Interface (MIPI), Inter-Integrated Circuit (I2C), Universal Serial Bus (USB), or another connection. The switched fabrics 214 a and 214 b may also provide power to the CPU packages 204 using various connections, including Peripheral Component Interconnect Express (PCIe), USB, or other connections. Although only two switched fabrics 214 a and 214 b are shown connecting the power supplies 215 to the sensors 212 and CPU packages 204, it is understood that the approach shown by FIG. 3 can be modified to include additional switched fabrics 214.

FIG. 4 is an example redundant data fabric for scene filtering using motion estimation. The redundant data fabric provides redundant data connection pathways between sensors 212 and CPU packages 204. In this example view, three CPU packages 204 a, 204 b, and 204 c are connected to three sensors 212 a, 212 b, and 212 c via three switched fabrics 213 a, 213 b, and 213 c. Each CPU package 204 a, 204 b, and 204 c is connected to a subset of the switched fabrics 213 a, 213 b, and 213 c. For example, CPU package 204 a is connected to switched fabrics 213 a and 213 c, CPU package 204 b is connected to switched fabrics 213 a and 213 b, and CPU package 204 c is connected to switched fabrics 213 b and 213 c. Each switched fabric 213 a, 213 b, and 213 c is connected to a subset of the sensors 212 a, 212 b, and 212 c. For example, switched fabric 213 a is connected to sensors 212 a and 212 b, switched fabric 213 b is connected to sensor 212 b and 212 c, and switched fabric 213 c is connected to sensors 212 a and 212 c. Under this topology, each CPU package 204 a, 204 b, and 204 c has an available connection path to any sensor 212 a, 212 b, and 212 c. It is understood that the topology of FIG. 4 is exemplary, and that CPU packages, switched fabrics, sensors, or connections between components may be added or removed while maintaining redundancy as can be appreciated by one skilled in the art.

FIG. 5 is an example view of process allocation across CPU packages for scene filtering using motion estimation. Shown are three CPU packages 204 a, 204 b, and 204 c. Each CPU package 204 a includes a processing unit that has been allocated (e.g., by a hypervisor 228 or other process or service) primary execution of a process and another processing unit that has been allocated secondary execution of a process. As set forth herein, primary execution of a process describes an executing instance of a process whose output will be provided to another process or service. Secondary execution of the process describes executing an instance of the process in parallel to the primary execution, but the output may not be output to the other process or service. For example, in CPU package 204 a, processing unit 502 a has been allocated secondary execution of “process B,” denoted as secondary process B 504 b, while processing unit 502 b has been allocated primary execution of “process C,” denoted as primary process C 506 a.

CPU package 204 a also comprises two redundant processing units that are not actively executing a process A, B, or C, but are instead reserved in case of failure of an active processing unit. Redundant processing unit 508 a has been reserved as “A/B redundant,” indicating that reserved processing unit 508 a may be allocated primary or secondary execution of processes A or B in the event of a failure of a processing unit allocated the primary or secondary execution of these processes. Redundant processing unit 508 b has been reserved as “A/C redundant,” indicating that reserved processing unit 508 b may be allocated primary or secondary execution of processes A or C in the event of a failure of a processing unit allocated the primary or secondary execution of these processes.

CPU package 204 b includes processing unit 502 c, which has been allocated primary execution of “process A,” denoted as primary process A 510 a, and processing unit 502 d, which has been allocated secondary execution of “process C,” denoted as secondary process C 506 b. CPU package 204 b also includes redundant processing unit 508 c, reserved as “A/B redundant,” and redundant processing unit 508 d, reserved as “B/C redundant.” CPU package 204 c includes processing unit 502 e, which has been allocated primary execution of “process B,” denoted as primary process B 504 a, and processing unit 502 f, which has been allocated secondary execution of “process A,” denoted as secondary process A 510 a. CPU package 204 c also includes redundant processing unit 508 e, reserved as “B/C redundant,” and redundant processing unit 508 f, reserved as “A/C redundant.”

As set forth in the example view of FIG. 5 , primary and secondary instances processes A, B, and C are each executed in an allocated processing unit. Thus, if a processing unit performing primary execution of a given process fails, the processing unit performing secondary execution may instead provide output of the given process to a receiving process or service. Moreover, the primary and secondary execution of a given process are executed on different CPU packages. Thus, if an entire processing unit fails, execution of each of the processes can continue using one or more processing units handling secondary execution. The redundant processing units 508 a-f allow for allocation of primary or secondary execution of a process in the event of processing unit failure. This further prevents errors caused by processing unit failure as parallel primary and secondary execution of a process may be restored. One skilled in the art would understand that the number of CPU packages, processing units, redundant processing units, and processes may be modified according to performance requirements while maintaining redundancy.

For further explanation, FIG. 6 sets forth a diagram of an execution environment 227 in accordance with some embodiments of the present disclosure. The execution environment 227 depicted in FIG. 6 may be embodied in a variety of different ways. The execution environment 227 may be provided, for example, by one or more cloud computing providers such as Amazon AWS, Microsoft Azure, Google Cloud, and others, including combinations thereof. Alternatively, the execution environment 227 may be embodied as a collection of devices (e.g., servers, storage devices, networking devices) and software resources that are included in a private data center. In fact, the execution environment 227 may be embodied as a combination of cloud resources and private resources that collectively form a hybrid cloud computing environment. Readers will appreciate that the execution environment 227 may be constructed in a variety of other ways and may even include resources within one or more autonomous vehicles or resources that communicate with one or more autonomous vehicles.

The execution environment 227 depicted in FIG. 6 may include storage resources 608, which may be embodied in many forms. For example, the storage resources 608 may include flash memory, hard disk drives, nano-RAM, 3D crosspoint non-volatile memory, MRAM, non-volatile phase-change memory (‘PCM’), storage class memory (‘SCM’), or many others, including combinations of the storage technologies described above. Readers will appreciate that other forms of computer memories and storage devices may be utilized as part of the execution environment 227, including DRAM, SRAM, EEPROM, universal memory, and many others. The storage resources 608 may also be embodied, in embodiments where the execution environment 227 includes resources offered by a cloud provider, as cloud storage resources such as Amazon Elastic Block Storage (‘EBS’) block storage, Amazon S3 object storage, Amazon Elastic File System (‘EFS’) file storage, Azure Blob Storage, and many others. The example execution environment 227 depicted in FIG. 6 may implement a variety of storage architectures, such as block storage where data is stored in blocks, and each block essentially acts as an individual hard drive, object storage where data is managed as objects, or file storage in which data is stored in a hierarchical structure. Such data may be saved in files and folders, and presented to both the system storing it and the system retrieving it in the same format.

The execution environment 227 depicted in FIG. 6 also includes communications resources 610 that may be useful in facilitating data communications between components within the execution environment 227, as well as data communications between the execution environment 227 and computing devices that are outside of the execution environment 227. Such communications resources may be embodied, for example, as one or more routers, network switches, communications adapters, and many others, including combinations of such devices. The communications resources 610 may be configured to utilize a variety of different protocols and data communication fabrics to facilitate data communications. For example, the communications resources 610 may utilize Internet Protocol (‘IP’) based technologies, fibre channel (‘FC’) technologies, FC over ethernet (‘FCoE’) technologies, InfiniBand (‘IB’) technologies, NVM Express (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologies, and many others. The communications resources 610 may also be embodied, in embodiments where the execution environment 227 includes resources offered by a cloud provider, as networking tools and resources that enable secure connections to the cloud as well as tools and resources (e.g., network interfaces, routing tables, gateways) to configure networking resources in a virtual private cloud. Such communications resources may be useful in facilitating data communications between components within the execution environment 227, as well as data communications between the execution environment 227 and computing devices that are outside of the execution environment 227 (e.g., computing devices that are included within an autonomous vehicle).

The execution environment 227 depicted in FIG. 6 also includes processing resources 612 that may be useful in useful in executing computer program instructions and performing other computational tasks within the execution environment 227. The processing resources 612 may include one or more application-specific integrated circuits (‘ASICs’) that are customized for some particular purpose, one or more central processing units (‘CPUs’), one or more digital signal processors (‘DSPs’), one or more field-programmable gate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or other form of processing resources 612. The processing resources 612 may also be embodied, in embodiments where the execution environment 227 includes resources offered by a cloud provider, as cloud computing resources such as one or more Amazon Elastic Compute Cloud (‘EC2’) instances, event-driven compute resources such as AWS Lambdas, Azure Virtual Machines, or many others.

The execution environment 227 depicted in FIG. 6 also includes software resources 613 that, when executed by processing resources 612 within the execution environment 227, may perform various tasks. The software resources 613 may include, for example, one or more modules of computer program instructions that when executed by processing resources 612 within the execution environment 227 are useful in training neural networks configured to determine control autonomous vehicle control operations. For example, a training module 614 may train a neural network using training data including sensor 212 data and control operations recorded or captured contemporaneous to the training data. In other words, the neural network may be trained to encode a relationship between an environment relative to an autonomous vehicle 100 as indicated in sensor 212 data and the corresponding control operations effected by a user or operation of the autonomous vehicle. The training module 614 may provide a corpus of training data, or a selected subset of training data, to train the neural network. For example, the training module 614 may select particular subsets of training data associated with particular driving conditions, environment states, etc. to train the neural network.

The software resources 613 may include, for example, one or more modules of computer program instructions that when executed by processing resources 612 within the execution environment 227 are useful in deploying software resources or other data to autonomous vehicles 100 via a network 618. For example, a deployment module 616 may provide software updates, neural network updates, or other data to autonomous vehicles 100 to facilitate autonomous vehicle control operations.

The software resources 613 may include, for example, one or more modules of computer program instructions that when executed by processing resources 612 within the execution environment 227 are useful in collecting data from autonomous vehicles 100 via a network 618. For example, a data collection module 620 may receive, from autonomous vehicles 100, collected sensor 212, associated control operations, software performance logs, or other data. Such data may facilitate training of neural networks via the training module 614 or stored using storage resources 608.

For further explanation, FIG. 7 sets forth a flow chart illustrating an exemplary method for scene filtering using motion estimation that includes identifying 702 (e.g., by a training module 614 of an execution environment 227), in camera data 704 from an autonomous vehicle 100, based on motion relative to the autonomous vehicle 100, one or more pixels. The camera data 704 may be included in a corpus of camera data 704 received from one or more autonomous vehicles 100. The camera data 704 may be provided to the execution environment 227 via a communications adapter 238 of the autonomous vehicle. The camera data 704 may be associated with data indicating one or more control operations performed by the autonomous vehicle 100 (e.g., effected by a driver or operator of the autonomous vehicle 100) contemporaneous to the camera data 704 being recorded or captured. The camera data 704 may comprise one or more images captured or generated by one or camera sensors 212. The camera data 704 may comprise a sequence of images or frames encoded as video data.

Identifying 702, in the camera data 704, based on motion relative to the autonomous vehicle 100, one or more pixels may include identifying, as the one or more pixels, one or more pixels associated with stationary motion and/or one or more pixels associated with motion away from the autonomous vehicle 100. Pixels in a given frame of the camera data 704 may be compared to corresponding in other frames (e.g., sequentially prior or subsequent to the given frame) to determine if the pixels are associated with an object that is stationary relative to the autonomous vehicle 100 (e.g., effectively stationary), or moving away from the autonomous vehicle 100. Pixels may be identified as being effectively stationary if a visual object in which they are included remains at a substantially same size (e.g., pixel height, width, and/or area) or at a substantially same region in the camera data 704. Pixels may be identified as moving away from the autonomous vehicle 100 if a visual object in which they are included decreases in size (e.g., pixel height, width, and/or area) over time as indicated in the camera data 704, or if the visual objects move to regions in the camera field of view that are closer to the camera (e.g., moving from an upper region of the field of view to a lower region of the field of view).

For example, assume an autonomous vehicle 100 traveling along a highway. The sky may be captured in the cameras of the autonomous vehicle 100. Objects such as the sky, the sun, or clouds may appear to be effectively stationary relative to the autonomous vehicle 100. Accordingly, the pixels in the camera data for the sky, sun, and/or clouds may be identified for inclusion in the one or more pixels due to their effectively stationary motion. Other vehicles on the road traveling away from the autonomous vehicle 100 (e.g., located in front of the autonomous vehicle 100 and traveling at a speed greater than the autonomous vehicle 100) would be captured as visual objects whose size is decreasing and/or are moving to regions in the camera field of view further from the camera (e.g., from a central or lower region to an upper region). Accordingly, the pixels for these vehicles would be identified for inclusion in the one or more pixels. Conversely, pixels for visual objects such as the road, lane markers, and vehicles coming closer to the automated vehicle 100 would not be included in the one or more pixels.

The method of FIG. 7 also includes filtering 706 (e.g., by the training module 614 of the execution environment 227), from the camera data 704, the one or more pixels. Filtering 706, from the camera data 704, the one or more pixels prevents neural networks training on the camera data 704 from training on the identified one or more pixels. Filtering 706 the one or more pictures may include blacking out the one or more pixels or replacing the one or more pixels with another default color value. Filtering 706 the one or more pixels may also include zeroing out an error function to be applied to the one or more pixels (e.g., during training of a neural network on the camera data 704).

The method of FIG. 7 also includes training 708 (e.g., by the training module 614 of the execution environment 227, based on the filtered camera data 704, a neural network. The trained neural network may be configured to determine, based on sensor 212 data including camera data 704, one or more autonomous vehicle control operations. Accordingly, training 708 the neural network may include providing, as training data, sensor data 212 including filtered camera data 704 from a plurality of automated vehicles 100 as well as corresponding control operations effected contemporaneous to the recording of the sensor data 212. Thus, the trained neural network may accept, as input, sensor data 212 including camera data and output one or more autonomous vehicle control operations.

As the identified one or more pixels have been filtered from the camera data 704, the neural network will not be trained on unimportant data or “noise” in the camera data 704 for determining autonomous vehicle control operations. Particularly, as pixels labeled as unimportant or noise are constant throughout the corpus of training data (e.g., blacked out, zeroed out via an error function), they are not factors in training the neural network and determining the corresponding control operations. For example, the state of the sky or positioning of the sun may be unimportant in determining autonomous vehicle control operations. Accordingly, this data is filtered from the camera data 704. Although it is possible to compensate for noise in the camera data 704 through a large enough sample size, the approach described herein allows for training the neural network without the need for such a large sample size, improving the efficiency in training the neural network.

Although the method of FIG. 7 is described in the context of being performed by an execution environment 227 (e.g., a training module 614), it is understood that the method of FIG. 7 may be performed at least partially by an autonomous vehicle 100. For example, the camera data 704 may be filtered prior to being provided by the autonomous vehicle 100 to the execution environment 227 for training of the neural network. Additionally, although the method of FIG. 7 describes camera data 704 as being captured and provided by an autonomous vehicle 100, it is understood that the camera data 704 may be captured and provided by vehicles without autonomous operational capabilities. For example, a vehicle lacking autonomous operational capabilities but equipped with cameras and potentially other sensors 212 may capture the camera data 704 and corresponding user-input control operations for providing to the execution environment 227.

For further explanation, FIG. 8 sets forth a flow chart illustrating an exemplary method for scene filtering using motion estimation that includes identifying 702 (e.g., by a training module 614 of an execution environment 227), in camera data 704 from an autonomous vehicle 100, based on motion relative to the autonomous vehicle 100, one or more pixels; filtering 706, from the camera data 704, the one or more pixels, and training 708, based on the filtered camera data 708, a neural network.

The method of FIG. 8 differs from FIG. 7 in that identifying 702, in camera data 704 from an autonomous vehicle 100, based on motion relative to the autonomous vehicle 100, one or more pixels also includes identifying 802, for each pixel of the camera data 704, a corresponding motion vector. Identifying 802, for each pixel of the camera data 704, a corresponding motion vector may comprise providing the camera data 704 to a neural network configured to identify, for a given pixel in a given frame, based on one or more other frames, a corresponding motion vector.

The method of FIG. 8 differs from FIG. 7 in that identifying 702, in camera data 704 from an autonomous vehicle 100, based on motion relative to the autonomous vehicle 100, one or more pixels also includes determining 804, based on each pixel and the corresponding motion vector, whether to include a respective pixel in the identified one or more pixels. For example, a pixel with a corresponding motion vector having a magnitude falling below a threshold may indicate that the pixel is included in a stationary or effectively stationary visual object relative. Accordingly, the pixel would be included in the identified one or more pixels. As another example, a pixel with a corresponding motion vector directed away from the camera (e.g., having a positive Y-axis component), indicating that the corresponding visual object is moving away from the automated vehicle 100, would be included in the identified one or more pixels. As another example, a pixel with a corresponding motion vector directed toward the camera (e.g., having a negative Y-axis component), indicating that the corresponding visual object is moving toward the automated vehicle 100, would not be included in the identified one or more pixels.

For further explanation, FIG. 9 sets forth a flow chart illustrating an exemplary method for scene filtering using motion estimation that includes identifying 702 (e.g., by a training module 614 of an execution environment 227), in camera data 704 from an autonomous vehicle 100, based on motion relative to the autonomous vehicle 100, one or more pixels; filtering 706, from the camera data 704, the one or more pixels, and training 708, based on the filtered camera data 708, a neural network.

The method of FIG. 9 differs from FIG. 7 in that the method of FIG. 9 also includes providing 902 the trained neural network 903 to one or more autonomous vehicles 100. For example, the trained neural network 903 may be provided to the one or more autonomous vehicles 100 via a network 618. The trained neural network 903 may be provided to the one or more autonomous vehicles 100 during installation, configuration, manufacture, or as part of an update of the autonomous vehicles 100 or automation computing systems 116. Thus, an automated vehicle 100 receiving the trained neural network 903 may be configured to determine automated vehicle control operations based on the trained neural network 903.

In view of the explanations set forth above, readers will recognize that the benefits of scene filtering using motion estimation according to embodiments of the present invention include:

-   -   Improved performance of training a neural network by filtering         irrelevant data and noise from camera data.     -   Improved performance of training a neural network by allowing         for the neural network to be trained on a smaller corpus of         training data than would be required to compensate for noise if         unfiltered.

Exemplary embodiments of the present invention are described largely in the context of a fully functional computer system for scene filtering using motion estimation. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a computer program product. Persons skilled in the art will recognize also that, although some of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present invention.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

It will be understood that any of the functionality or approaches set forth herein may be facilitated at least in part by artificial intelligence applications, including machine learning applications, big data analytics applications, deep learning, and other techniques. Applications of such techniques may include: machine and vehicular object detection, identification and avoidance; visual recognition, classification and tagging; algorithmic financial trading strategy performance management; simultaneous localization and mapping; predictive maintenance of high-value machinery; prevention against cyber security threats, expertise automation; image recognition and classification; question answering; robotics; text analytics (extraction, classification) and text generation and translation; and many others.

It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present invention without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present invention is limited only by the language of the following claims. 

What is claimed is:
 1. A method for scene filtering using motion estimation, comprising: selecting, from a plurality of pixels in camera data from an autonomous vehicle, by a computing device other than the autonomous vehicle, one or more pixels corresponding to one or more objects appearing effectively stationary relative to the autonomous vehicle; blacking out, in the camera data by the computing device, the one or more pixels; and training, by the computing device and based on the camera data including the blacked out one or more pixels, a neural network.
 2. The method of claim 1, wherein identifying the one or more pixels comprises identifying, for inclusion in the one or more pixels, one or more pixels associated motion away from the autonomous vehicle.
 3. The method of claim 1, wherein identifying the one or more pixels comprises: identifying, for each pixel of the camera data, a corresponding motion vector; and determining, based on each pixel and the corresponding motion vector, whether to include a respective pixel in the identified one or more pixels.
 4. The method of claim 3, wherein identifying, for each pixel of the camera data, the corresponding motion vector comprises providing the video data to another neural network.
 5. The method of claim 1, further comprising providing the trained neural network to one or more autonomous vehicles.
 6. The method of claim 1, wherein the trained neural network is configured to determine, based on sensor data, one or more autonomous vehicle control operations.
 7. The method of claim 1, wherein training the machine learning model comprises providing, to the machine learning model, the camera data including the blacked out one or more pixels and one or more control operations of the autonomous vehicle corresponding to the filtered sensor data.
 8. An apparatus for scene filtering using motion estimation, the apparatus configured to perform steps comprising: selecting, from a plurality of pixels in camera data from an autonomous vehicle, by a computing device other than the autonomous vehicle, one or more pixels corresponding to one or more objects appearing effectively stationary relative to the autonomous vehicle; blacking out, in the camera data by the computing device, the one or more pixels; and training, by the computing device and based on the camera data including the blacked out one or more pixels, a neural network.
 9. The apparatus of claim 8, wherein identifying the one or more pixels comprises identifying, for inclusion in the one or more pixels, one or more pixels associated motion away from the autonomous vehicle.
 10. The apparatus of claim 8, wherein identifying the one or more pixels comprises: identifying, for each pixel of the camera data, a corresponding motion vector; and determining, based on each pixel and the corresponding motion vector, whether to include a respective pixel in the identified one or more pixels.
 11. The apparatus of claim 10, wherein identifying, for each pixel of the camera data, the corresponding motion vector comprises providing the video data to another neural network.
 12. The apparatus of claim 8, wherein the steps further comprise providing the trained neural network to one or more autonomous vehicles.
 13. The apparatus of claim 8, wherein the trained neural network is configured to determine, based on sensor data, one or more autonomous vehicle control operations.
 14. The apparatus of claim 8, wherein training the machine learning model comprises providing, to the machine learning model, the camera data including the blacked out one or more pixels and one or more control operations of the autonomous vehicle corresponding to the filtered sensor data.
 15. A computer program product disposed upon a non-transitory computer readable medium, the computer program product comprising computer program instructions for scene filtering using motion estimation that, when executed, cause a computer system to carry out the steps of: selecting, from a plurality of pixels in camera data from an autonomous vehicle, by a computing device other than the autonomous vehicle, one or more pixels corresponding to one or more objects appearing effectively stationary relative to the autonomous vehicle; blacking out, in the camera data by the computing device, the one or more pixels; and training, by the computing device and based on the camera data including the blacked out one or more pixels, a neural network.
 16. The computer program product of claim 15, wherein identifying the one or more pixels comprises identifying, for inclusion in the one or more pixels, one or more pixels associated motion away from the autonomous vehicle.
 17. The computer program product of claim 15, wherein identifying the one or more pixels comprises: identifying, for each pixel of the camera data, a corresponding motion vector; and determining, based on each pixel and the corresponding motion vector, whether to include a respective pixel in the identified one or more pixels.
 18. The computer program product of claim 17, wherein identifying, for each pixel of the camera data, the corresponding motion vector comprises providing the video data to another neural network.
 19. The computer program product of claim 15, wherein the steps further comprise providing the trained neural network to one or more autonomous vehicles.
 20. The computer program product of claim 15, wherein training the machine learning model comprises providing, to the machine learning model, the camera data including the blacked out one or more pixels and one or more control operations of the autonomous vehicle corresponding to the filtered sensor data. 